1. Statement of the Technical Field
The inventive arrangements relate to three-dimensional point cloud data for terrain modeling, and more particularly to methods for evaluating and recording the significance of data points within the point cloud for improved management of dense, potentially diverse terrain survey data.
2. Description of the Related Art
Three dimensional scanning systems can measure in an automatic way a large number of points forming the surface of the earth, or some other object, and can output a point cloud as a data file. The point cloud represents the set of points in a three dimensional space that the device has measured with respect to a set of coordinate axes. One example of a three dimensional scanning system is LIDAR (Light Detection And Ranging). LIDAR is an optical remote sensing technology that can measure the distance to a target by illuminating the target with light, often using pulses from a laser. LIDAR data has proven its worth, consistently producing accurate and detailed results across many applications including those associated with environmental, engineering and forestry. Although LIDAR has many advantages, there are certain problems associated with its use. One such problem is the very large quantity of information usually associated with point cloud data. In fact, point cloud data has consistently proven to be problematic due to its enormous density and volume. As a result, most applications perform rendering operations by means of an interpolation process that produces a new representation of the data. The new representation is typically produced in the form of a regular or periodic grid of data points, but this process can be destructive of important information contained in the data.
In order to appreciate the volume and density of information associated with LIDAR data, it may be noted that ground spacing between points associated with a LIDAR terrain scan can be 3 cm or even smaller in some cases. Thus, for any appreciable size terrain area, points can number in the hundreds of millions to billions. Files (usually multiple files) for such data are measured in gigabytes. Model significant point derivation schemes (sometimes referred to herein as point selection or data thinning schemes) can be used to reduce the volume of point cloud data. However, the various conventional schemes that have been devised in the art have not been found to provide completely satisfactory results. For example, decimation schemes thin data by simply eliminating every nth data point without any consideration as to the significance of such points. In this regard, a decimation scheme may select only every 10th point, or 50th point, for inclusion in a data set, and will eliminate all others. One problem with this approach is that it can and will miss significant points in the terrain, such as peaks in hills and bottoms of valleys in the terrain. Another type of established thinning or point selection is a grid digital elevation model (DEM) scheme. However, grid schemes require a (possibly arbitrary)_selection of an appropriate grid size for the thinning process. Even so, such schemes may still select only the highest/lowest points in each grid or may compute an interpolated value at a post or grid cell midpoint based on very dissimilar point values. Accordingly, a grid scheme will also inevitably miss significant points within each grid, or will be subject to the limitations of the interpolation scheme used. Avoidance of these problems requires an extremely small post spacing of points in a DEM and the size of these DEM files themselves then become a processing issue. Compression methods can apparently be used with good results when dealing with point cloud data. Still, these compression methods often require conversion to and from the compressed form to perform work with the data, and do not seek to remove truly redundant or insignificant points from the scheme.